class_docstr: Deterministic/Degenerate distribution.
  Parameters
  ----------
  value
      Tensor containing the values, of shape `(*batch_shape, *event_shape)`.
  F
code: def sample(
      self, num_samples: Optional[int] = None, dtype=np.float32
  ) -> Tensor:
      return _sample_multiple(
          lambda value: value, value=self.value, num_samples=num_samples
      ).astype(dtype=dtype)
  
imports: from typing import Dict, List, Optional, Tuple, Type
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 10 for calling the Deterministic obj's underlying member method code with example usage: obj.sample(num_samples, dtype) and returns: _sample_multiple